Multiscale analysis of immune profiling data from dengue and chikungunya virus infections in humans

对人类登革热和基孔肯雅病毒感染的免疫分析数据进行多尺度分析

• 批准号: 9333951
• 负责人: Theodore Robertson Pak
• 金额: $ 4.12万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-13 至 2020-06-12
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9333951
• 关键词: Acute; Adult; Antibodies; Antiviral Agents; Antiviral Therapy; Automobile Driving; Bayesian Analysis; Behavior; Big Data; Binding; Biochemical Pathway; Bioinformatics; Biological; Biological Assay; Biological Markers; Blood specimen; Cell model; Cells; Cessation of life; Chikungunya virus; Childhood; Chronic; Clinical; Communicable Diseases; Complex; Computer Simulation; Culicidae; Data; Data Analyses; Data Sources; Databases; Dendritic Cells; Dengue; Dengue Infection; Dengue Vaccine; Dengue Virus; Dependence; Development; Diagnostic tests; Disease; Enrollment; Fatigue; Flowers; Funding; Future; Genes; Genetic; Genetic Transcription; Genets; Genome; Genomics; Global Change; Human; Immune; Immune response; Individual; Infection; Innate Immune System; Interferon Type I; Interferons; Lead; Longitudinal cohort; Maps; Measurement; Measures; Methods; Modeling; Molecular; Monitor; Multivariate Analysis; Nicaragua; Noise; Outcome; Pathogenesis; Pathogenicity; Pathway Analysis; Pathway interactions; Patients; Persons; Polyarthritides; Population; Predisposition; Process; Proteomics; Public Health; Quantitative Trait Loci; Recording of previous events; Regulator Genes; Reporting; Research; Resolution; Risk; Role; Running; Serotyping; Serum; Shock; Side; Signal Transduction; Signaling Protein; Small Interfering RNA; Standardization; Subgroup; Symptoms; Syndrome; System; Systems Biology; Techniques; Technology; Testing; Therapeutic; Time; Tissues; Transcript; Uncertainty; Vaccines; Validation; Variant; Viral; Viral Hemorrhagic Fevers; Viral Proteins; Virus; Virus Diseases; Whole Organism; biomarker selection; cell type; chikungunya; cytokine; diagnostic assay; disabling symptom; experimental study; genetic variant; global health; infectious disease model; insight; member; model design; monocyte; network models; next generation sequencing; novel; pathogen; predictive marker; respiratory; screening; transcriptome sequencing; transcriptomics; translational diagnostics; transmission process; vaccine discovery; vector mosquito; viral transmission; virus host interaction; virus pathogenesis

PROJECT SUMMARY Dengue virus (DENV) is a significant threat to public health, transmitting via mosquitos and infecting hundreds of millions around the world each year, of which roughly one hundred million become symptomatic and one half-million develop severe complications such as hemorrhagic fever, shock syndrome, or death. Despite many efforts to interpret antibody and serum cytokine measurements, only once has a diagnostic test been able to differentiate patients that are susceptible to heterotypic infections that associate with greater risk for severe complications. The uncertainty of the dynamics of the immune response to DENV has repeatedly thwarted efforts to create a vaccine protecting against all prevalent serotypes for over 80 years. A perhaps even more urgent problem is the blossoming spread of chikungunya virus (CHIKV), another mosquito-borne virus characterized by higher transmissibility and infection rates much higher than those of DENV, with roughly three quarters of infected persons developing symptoms that can include chronic polyarthritis and fatigue. Sharing the same urban mosquito vectors as DENV, the transmission of CHIKV within the Western hemisphere was reported for the first time two years ago. Like DENV, there are no approved specific treatments or vaccines. So far, very little is known about the molecular interactions necessary for CHIKV to enter human cells and effectively counter the innate immune system. Next generation sequencing and immune profiling technologies such as RNA-seq, Luminex, proteomics, and CyTOF have the ability to generate a wealth of data that can be used to help illuminate global biomolecular changes driving viral infections in humans, but only if signal can be separated from noise to identify useful signatures and key pathways. The pathogenesis of an infection within a host is a complex process, involving interactions among networks of biomolecules, cell types, tissues, and host individuals. Such complexity necessitates a multiscale, integrative approach, since characterizing one network or phenomenon in isolation is unlikely to sufficiently explain changes occurring across the entire system. This study proposes analyses that will identify robust biomarkers derived from immune profiling of a longitudinal cohort of pediatric DENV and CHIKV infections in Nicaragua, as part of a multi-institutional consortium (DHIPC). Furthermore, we will integrate these data into causal network models of the host- pathogen interaction, which will reveal key driver genes for pathways that associate with changes in the host immune response and facilitate antiviral and vaccine discovery. Given the anticipated data, this proposal maximizes the impact of the modeling approach for DENV and CHIKV on future biological discovery and advances the state of the art in holistic, data-driven modeling of infectious disease.

项目摘要 登革热病毒（DENV）是对公共卫生的重大威胁，通过蚊子传播并感染了数百种 每年全球数百万，其中大约一亿个症状，一亿 有50万人出现严重的并发症，例如出血热，休克综合征或死亡。尽管有很多 解释抗体和血清细胞因子测量的努力，只有一次诊断测试才能 区分容易受到异型感染的患者，这些感染与严重的风险更大 并发症。免疫反应对DENV的动力学的不确定性反复挫败 努力制造疫苗保护所有普遍的血清型已有80多年的历史。 也许更紧急的问题是奇京根尼亚病毒（Chikv）的蔓延，另一种 蚊子传播的病毒的特征是可传播性和感染率较高，远高于 DENV，大约四分之三的感染者患有可能包括慢性的症状 多关节炎和疲劳。共享与Denv的同一城市蚊子向量，Chikv在 两年前，西半球首次据报道。像DENV一样，没有批准 具体治疗或疫苗。到目前为止，关于分子相互作用所需的分子相互作用很少 CHIKV进入人类细胞，并有效地对抗先天免疫系统。 下一代测序和免疫分析技术，例如RNA-Seq，Luminex， 蛋白质组学和Cytof具有生成可用于阐明全球的大量数据 生物分子变化驱动人类病毒感染，但前提是可以将信号从噪声分离到 确定有用的签名和关键途径。宿主内感染的发病机理是一个复杂的 过程，涉及生物分子，细胞类型，组织和宿主个体网络之间的相互作用。这样的 复杂性需要一种多尺度，整合的方法，因为表征一个网络或现象 隔离不可能充分解释整个系统中发生的变化。 这项研究提出了分析，该分析将鉴定出从A的免疫分析中得出的强大生物标志物 尼加拉瓜的小儿DENV和CHIKV感染的纵向队列，作为多机构的一部分 财团（DHIPC）。此外，我们将将这些数据集成到主机的因果网络模型中 病原体相互作用，这将揭示与宿主变化相关的途径的关键驱动基因 免疫反应并促进抗病毒和疫苗发现。鉴于预期的数据，该提案 最大化建模方法对DENV和CHIKV对未来生物学发现的影响 在整体，数据驱动的传染病建模中进步。